Time-frequency analysis of speech signals using the Stockwell transform for the detection of upper respiratory tract infection

The acoustic properties of speech demonstrate modifications in the presence of different health states. Biomedical engineering has great promise for creating non-invasive diagnostic processes that use speech as a biomarker. The use of speech indications to screen for upper respiratory tract infections (URTIs), such as the common cold, may have potential advantages in terms of limiting transmission. In this study, we have employed the Stockwell transform-based time-frequency (TF) analysis of speech signals for URTI detection. The Stockwell transform is applied on speech signals to derive their TF representation. Using a TF matrix, the various statistics of magnitude and phase are calculated and used as features for classifying speech of healthy speakers and speakers with URTI. The URTIC database is employed for evaluating the proposed features. The utilization of an ensemble of support vector machines (SVM) is proposed as a classification approach to address the issue of class imbalance. The results show that the proposed method produces comparable outcomes to state-of-the-art approaches. The proposed features obtain 66.53% and 64.65% UARs on the development and test partitions of the URTIC database.